Uncovering the brain's biggest secret - Melanie E. Peffer

In the late 1860s, scientists believed they were on the verge of uncovering the brain’s biggest secret. They already knew the brain controlled the body through electrical impulses. The question was, how did these signals travel through the body without changing or degrading? It seemed that perfectly transmitting these impulses would require them to travel uninterrupted along some kind of tissue. This idea, called reticular theory, imagined the nervous system as a massive web of tissue that physically connected every nerve cell in the body. Reticular theory captivated the field with its elegant simplicity.

But soon, a young artist would cut through this conjecture and sketch a bold new vision of how our brains work. Sixty years before reticular theory was born, developments in microscope technology revealed cells to be the building blocks of organic tissue. This finding was revolutionary, but early microscopes struggled to provide additional details. The technology was especially challenging for researchers studying the brain. Soft nervous tissue was delicate and difficult to work with. And even when researchers were able to get it under the microscope, the tissue was so densely packed it was impossible to see much.

To improve their view, scientists began experimenting with special staining techniques designed to provide clarity through contrast. The most effective came courtesy of Camillo Golgi in 1873. First, Golgi hardened the brain tissue with potassium bichromate to prevent cells from deforming during handling. Then he doused the tissue in silver nitrate, which visibly accumulated in nerve cells. Known as the “black reaction,” Golgi’s Method finally allowed researchers to see the entire cell body of what would later be named the neuron. The stain even highlighted the fibrous branches that shot off from the cell in different directions.

Images of these branches became hazy at the ends, making it difficult to determine exactly how they fit into the larger network. But Golgi concluded that these branches connected, forming a web of tissue comprising the entire nervous system. Fourteen years later, a young scientist and aspiring artist named Santiago Ramón y Cajal began to build on Golgi’s work. While writing a book about microscopic imaging, he came across a picture of a cell treated with Golgi’s stain. Cajal was in awe of its exquisite detail—both as a scientist and an artist. He soon set out to improve Golgi’s stain even further and create more detailed references for his artwork.

By staining the tissue twice in a specific time frame, Cajal found he could stain a greater number of neurons with better resolution. And what these new slides revealed would upend reticular theory—the branches reaching out from each nerve cell were not physically connected to any other tissue. So how were these individual cells transmitting electrical signals? By studying and sketching them countless times, Cajal developed a bold, new hypothesis. Instead of electrical signals traveling uninterrupted across a network of fibers, he proposed that signals were somehow jumping from cell to cell in a linear chain of activation.

The idea that electrical signals could travel this way was completely unheard of when Cajal proposed it in 1889. However, his massive collection of drawings supported his hypothesis from every angle. And in the mid-1900s, electron microscopy further supported this idea by revealing a membrane around each nerve cell keeping it separate from its neighbors. This formed the basis of the “neuron doctrine,” which proposed the brain’s tissue was made up of many discrete cells, instead of one connected tissue. The neuron doctrine laid the foundation for modern neuroscience and allowed later researchers to discover that electrical impulses are constantly converted between chemical and electrical signals as they travel from neuron to neuron.

Both Golgi and Cajal received the Nobel Prize for their separate, but shared discoveries, and researchers still apply their theories and methods today. In this way, their legacies remain connected as discrete elements in a vast network of knowledge.